setwd("/Users/chengg/Google Drive/EPICON/Mycobiome/Fungal ITS/statistic/Total.fungi")
library(vegan)
## Loading required package: permute
## Loading required package: lattice
## This is vegan 2.5-3
library(betapart)
library(colorRamps)
source("weighted Fst function.r")
rm(list = ls())
load("EPICON.data.preparation.RC.bNTI.ghost.2019.03.17.Rdata")
mean(Lagg[Lagg$TP<8 & Lagg$Habitat=="Leaf" & Lagg$Treatment=="Control",]$jtudisper)
## [1] 0.4409758
mean(Lagg[Lagg$TP>8 & Lagg$Habitat=="Leaf" & Lagg$Treatment=="Control",]$jtudisper)
## [1] 0.3205118
# bray
fung1<-fung[env$Treatment1=="Control"& env$TP>0,]
env1<-env[env$Treatment1=="Control"& env$TP>0,]
par(mfrow=c(1,4),mar=c(2, 2, 0.5, 0.5))
for (i in c("Leaf","Root", "Rhizosphere", "Soil")){
fungx<-fung1[env1$Treatment1== "Control" & env1$Habitat== i,]
envx<-env1[env1$Treatment1== "Control" & env1$Habitat== i,]
fung.dist<-vegdist(decostand(fungx,"hellinger"),"bray")
#fungx[fungx>0]=1
#fd<-beta.pair(fungx, index.family = "jaccard")
#fung.dist<-fd$beta.jtu
bd<-betadisper(fung.dist,factor(envx$TP))
print (anova(bd)); print(adonis(fung.dist~envx$TP))
plot(bd, col=blue2red(17)[1:17],
hull = FALSE,cex = 0.8,label.cex = 0.8,
seg.col=blue2red(17)[1:17], main="", xlab="PC1", ylab="PC2")
#boxplot(bd)
#print(TukeyHSD(bd))
}
## Analysis of Variance Table
##
## Response: Distances
## Df Sum Sq Mean Sq F value Pr(>F)
## Groups 16 1.0561 0.066009 11.701 2.385e-15 ***
## Residuals 85 0.4795 0.005641
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Call:
## adonis(formula = fung.dist ~ envx$TP)
##
## Permutation: free
## Number of permutations: 999
##
## Terms added sequentially (first to last)
##
## Df SumsOfSqs MeanSqs F.Model R2 Pr(>F)
## envx$TP 1 5.1818 5.1818 44.203 0.30653 0.001 ***
## Residuals 100 11.7228 0.1172 0.69347
## Total 101 16.9047 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Analysis of Variance Table
##
## Response: Distances
## Df Sum Sq Mean Sq F value Pr(>F)
## Groups 16 0.19982 0.0124889 6.3803 4.217e-09 ***
## Residuals 85 0.16638 0.0019574
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Call:
## adonis(formula = fung.dist ~ envx$TP)
##
## Permutation: free
## Number of permutations: 999
##
## Terms added sequentially (first to last)
##
## Df SumsOfSqs MeanSqs F.Model R2 Pr(>F)
## envx$TP 1 3.8049 3.8049 37.736 0.27398 0.001 ***
## Residuals 100 10.0829 0.1008 0.72602
## Total 101 13.8878 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Analysis of Variance Table
##
## Response: Distances
## Df Sum Sq Mean Sq F value Pr(>F)
## Groups 16 0.009434 0.00058965 0.6823 0.8036
## Residuals 85 0.073453 0.00086416
##
## Call:
## adonis(formula = fung.dist ~ envx$TP)
##
## Permutation: free
## Number of permutations: 999
##
## Terms added sequentially (first to last)
##
## Df SumsOfSqs MeanSqs F.Model R2 Pr(>F)
## envx$TP 1 5.1894 5.1894 48.013 0.32439 0.001 ***
## Residuals 100 10.8083 0.1081 0.67561
## Total 101 15.9977 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Analysis of Variance Table
##
## Response: Distances
## Df Sum Sq Mean Sq F value Pr(>F)
## Groups 16 0.03434 0.0021463 1.1348 0.3377
## Residuals 85 0.16076 0.0018913
##
## Call:
## adonis(formula = fung.dist ~ envx$TP)
##
## Permutation: free
## Number of permutations: 999
##
## Terms added sequentially (first to last)
##
## Df SumsOfSqs MeanSqs F.Model R2 Pr(>F)
## envx$TP 1 1.3668 1.36682 12.436 0.1106 0.001 ***
## Residuals 100 10.9912 0.10991 0.8894
## Total 101 12.3580 1.0000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
# jaccard
fung1<-fung[env$Treatment1=="Control"& env$TP>0,]
env1<-env[env$Treatment1=="Control"& env$TP>0,]
par(mfrow=c(1,4),mar=c(2, 2, 0.5, 0.5))
for (i in c("Leaf","Root", "Rhizosphere", "Soil")){
fungx<-fung1[env1$Treatment1== "Control" & env1$Habitat== i,]
envx<-env1[env1$Treatment1== "Control" & env1$Habitat== i,]
fung.dist<-vegdist(decostand(fungx,"hellinger"),"jaccard")
#fungx[fungx>0]=1
#fd<-beta.pair(fungx, index.family = "jaccard")
#fung.dist<-fd$beta.jtu
bd<-betadisper(fung.dist,factor(envx$TP))
print (anova(bd)); print(adonis(fung.dist~envx$TP))
plot(bd, col=blue2red(17)[1:17],
hull = FALSE,cex = 0.8,label.cex = 0.8,
seg.col=blue2red(17)[1:17], main="", xlab="PC1", ylab="PC2")
#boxplot(bd)
#print(TukeyHSD(bd))
}
## Analysis of Variance Table
##
## Response: Distances
## Df Sum Sq Mean Sq F value Pr(>F)
## Groups 16 1.06612 0.066632 15.278 < 2.2e-16 ***
## Residuals 85 0.37072 0.004361
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Call:
## adonis(formula = fung.dist ~ envx$TP)
##
## Permutation: free
## Number of permutations: 999
##
## Terms added sequentially (first to last)
##
## Df SumsOfSqs MeanSqs F.Model R2 Pr(>F)
## envx$TP 1 5.9376 5.9376 30.767 0.23528 0.001 ***
## Residuals 100 19.2986 0.1930 0.76472
## Total 101 25.2362 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Analysis of Variance Table
##
## Response: Distances
## Df Sum Sq Mean Sq F value Pr(>F)
## Groups 16 0.21099 0.0131872 6.8241 1.058e-09 ***
## Residuals 85 0.16426 0.0019324
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Call:
## adonis(formula = fung.dist ~ envx$TP)
##
## Permutation: free
## Number of permutations: 999
##
## Terms added sequentially (first to last)
##
## Df SumsOfSqs MeanSqs F.Model R2 Pr(>F)
## envx$TP 1 4.060 4.0600 21.588 0.17755 0.001 ***
## Residuals 100 18.807 0.1881 0.82245
## Total 101 22.867 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Analysis of Variance Table
##
## Response: Distances
## Df Sum Sq Mean Sq F value Pr(>F)
## Groups 16 0.009396 0.00058722 0.6865 0.7996
## Residuals 85 0.072703 0.00085533
##
## Call:
## adonis(formula = fung.dist ~ envx$TP)
##
## Permutation: free
## Number of permutations: 999
##
## Terms added sequentially (first to last)
##
## Df SumsOfSqs MeanSqs F.Model R2 Pr(>F)
## envx$TP 1 5.3206 5.3206 26.531 0.20968 0.001 ***
## Residuals 100 20.0547 0.2005 0.79032
## Total 101 25.3753 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Analysis of Variance Table
##
## Response: Distances
## Df Sum Sq Mean Sq F value Pr(>F)
## Groups 16 0.028354 0.0017721 1.1463 0.328
## Residuals 85 0.131409 0.0015460
##
## Call:
## adonis(formula = fung.dist ~ envx$TP)
##
## Permutation: free
## Number of permutations: 999
##
## Terms added sequentially (first to last)
##
## Df SumsOfSqs MeanSqs F.Model R2 Pr(>F)
## envx$TP 1 1.6762 1.67618 8.3053 0.07668 0.001 ***
## Residuals 100 20.1821 0.20182 0.92332
## Total 101 21.8583 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
# turnover
fung1<-fung[env$Treatment1=="Control"& env$TP>0,]
env1<-env[env$Treatment1=="Control"& env$TP>0,]
par(mfrow=c(1,4),mar=c(2, 2, 0.5, 0.5))
for (i in c("Leaf","Root", "Rhizosphere", "Soil")){
fungx<-fung1[env1$Treatment1== "Control" & env1$Habitat== i,]
envx<-env1[env1$Treatment1== "Control" & env1$Habitat== i,]
#fung.dist<-vegdist(decostand(fungx,"hellinger"),"bray")
fungx[fungx>0]=1
fd<-beta.pair(fungx, index.family = "jaccard")
fung.dist<-fd$beta.jtu
bd<-betadisper(fung.dist,factor(envx$TP))
print (anova(bd)); print(adonis(fung.dist~envx$TP))
plot(bd, col=blue2red(17)[1:17],
hull = FALSE,cex = 0.8,label.cex = 0.8,
seg.col=blue2red(17)[1:17], main="", xlab="PC1", ylab="PC2")
#boxplot(bd)
#print(TukeyHSD(bd))
}
## Analysis of Variance Table
##
## Response: Distances
## Df Sum Sq Mean Sq F value Pr(>F)
## Groups 16 0.57756 0.036098 9.4152 6.713e-13 ***
## Residuals 85 0.32589 0.003834
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Call:
## adonis(formula = fung.dist ~ envx$TP)
##
## Permutation: free
## Number of permutations: 999
##
## Terms added sequentially (first to last)
##
## Df SumsOfSqs MeanSqs F.Model R2 Pr(>F)
## envx$TP 1 4.260 4.2600 24.445 0.19643 0.001 ***
## Residuals 100 17.427 0.1743 0.80357
## Total 101 21.687 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Analysis of Variance Table
##
## Response: Distances
## Df Sum Sq Mean Sq F value Pr(>F)
## Groups 16 0.068274 0.0042671 1.9817 0.02346 *
## Residuals 85 0.183029 0.0021533
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Call:
## adonis(formula = fung.dist ~ envx$TP)
##
## Permutation: free
## Number of permutations: 999
##
## Terms added sequentially (first to last)
##
## Df SumsOfSqs MeanSqs F.Model R2 Pr(>F)
## envx$TP 1 3.9919 3.9919 26.33 0.20842 0.001 ***
## Residuals 100 15.1610 0.1516 0.79158
## Total 101 19.1530 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Analysis of Variance Table
##
## Response: Distances
## Df Sum Sq Mean Sq F value Pr(>F)
## Groups 16 0.083968 0.0052480 2.3945 0.005256 **
## Residuals 85 0.186295 0.0021917
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Call:
## adonis(formula = fung.dist ~ envx$TP)
##
## Permutation: free
## Number of permutations: 999
##
## Terms added sequentially (first to last)
##
## Df SumsOfSqs MeanSqs F.Model R2 Pr(>F)
## envx$TP 1 3.7318 3.7318 21.769 0.17878 0.001 ***
## Residuals 100 17.1426 0.1714 0.82122
## Total 101 20.8744 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Analysis of Variance Table
##
## Response: Distances
## Df Sum Sq Mean Sq F value Pr(>F)
## Groups 16 0.04436 0.0027725 2.1029 0.01522 *
## Residuals 85 0.11207 0.0013184
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Call:
## adonis(formula = fung.dist ~ envx$TP)
##
## Permutation: free
## Number of permutations: 999
##
## Terms added sequentially (first to last)
##
## Df SumsOfSqs MeanSqs F.Model R2 Pr(>F)
## envx$TP 1 1.1916 1.19159 6.9082 0.06462 0.001 ***
## Residuals 100 17.2490 0.17249 0.93538
## Total 101 18.4406 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
# nestedness
fung1<-fung[env$Treatment1=="Control"& env$TP>0,]
env1<-env[env$Treatment1=="Control"& env$TP>0,]
par(mfrow=c(1,4),mar=c(2, 2, 0.5, 0.5))
for (i in c("Leaf","Root", "Rhizosphere", "Soil")){
fungx<-fung1[env1$Treatment1== "Control" & env1$Habitat== i,]
envx<-env1[env1$Treatment1== "Control" & env1$Habitat== i,]
#fung.dist<-vegdist(decostand(fungx,"hellinger"),"bray")
fungx[fungx>0]=1
fd<-beta.pair(fungx, index.family = "jaccard")
fung.dist<-fd$beta.jne
bd<-betadisper(fung.dist,factor(envx$TP))
print (anova(bd)); print(adonis(fung.dist~envx$TP))
plot(bd, col=blue2red(17)[1:17],
hull = FALSE,cex = 0.8,label.cex = 0.8,
seg.col=blue2red(17)[1:17], main="", xlab="PC1", ylab="PC2")
#boxplot(bd)
#print(TukeyHSD(bd))
}
## Analysis of Variance Table
##
## Response: Distances
## Df Sum Sq Mean Sq F value Pr(>F)
## Groups 16 0.055609 0.0034756 1.5102 0.115
## Residuals 85 0.195620 0.0023014
##
## Call:
## adonis(formula = fung.dist ~ envx$TP)
##
## Permutation: free
## Number of permutations: 999
##
## Terms added sequentially (first to last)
##
## Df SumsOfSqs MeanSqs F.Model R2 Pr(>F)
## envx$TP 1 -0.01392 -0.0139173 -2.5726 -0.02641 0.981
## Residuals 100 0.54098 0.0054098 1.02641
## Total 101 0.52707 1.00000
## Analysis of Variance Table
##
## Response: Distances
## Df Sum Sq Mean Sq F value Pr(>F)
## Groups 16 0.024061 0.00150382 1.9199 0.02917 *
## Residuals 85 0.066577 0.00078326
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Call:
## adonis(formula = fung.dist ~ envx$TP)
##
## Permutation: free
## Number of permutations: 999
##
## Terms added sequentially (first to last)
##
## Df SumsOfSqs MeanSqs F.Model R2 Pr(>F)
## envx$TP 1 -0.09444 -0.094441 -17.48 -0.21183 1
## Residuals 100 0.54028 0.005403 1.21183
## Total 101 0.44584 1.00000
## Analysis of Variance Table
##
## Response: Distances
## Df Sum Sq Mean Sq F value Pr(>F)
## Groups 16 0.021502 0.00134390 2.2826 0.007928 **
## Residuals 85 0.050044 0.00058876
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Call:
## adonis(formula = fung.dist ~ envx$TP)
##
## Permutation: free
## Number of permutations: 999
##
## Terms added sequentially (first to last)
##
## Df SumsOfSqs MeanSqs F.Model R2 Pr(>F)
## envx$TP 1 -0.042662 -0.042662 -19.292 -0.23904 1
## Residuals 100 0.221134 0.002211 1.23904
## Total 101 0.178472 1.00000
## Analysis of Variance Table
##
## Response: Distances
## Df Sum Sq Mean Sq F value Pr(>F)
## Groups 16 0.008802 0.00055015 1.102 0.3665
## Residuals 85 0.042435 0.00049923
##
## Call:
## adonis(formula = fung.dist ~ envx$TP)
##
## Permutation: free
## Number of permutations: 999
##
## Terms added sequentially (first to last)
##
## Df SumsOfSqs MeanSqs F.Model R2 Pr(>F)
## envx$TP 1 -0.006759 -0.0067595 -5.8369 -0.06199 1
## Residuals 100 0.115806 0.0011581 1.06199
## Total 101 0.109046 1.00000
#
# bray
fung1<-fung[env$Treatment1=="Pre_flowering_drought"& env$TP>0,]
env1<-env[env$Treatment1=="Pre_flowering_drought"& env$TP>0,]
par(mfrow=c(1,4),mar=c(2, 2, 0.5, 0.5))
for (i in c("Leaf","Root", "Rhizosphere", "Soil")){
fungx<-fung1[env1$Treatment1== "Pre_flowering_drought" & env1$Habitat== i,]
envx<-env1[env1$Treatment1== "Pre_flowering_drought" & env1$Habitat== i,]
fung.dist<-vegdist(decostand(fungx,"hellinger"),"bray")
#fungx[fungx>0]=1
#fd<-beta.pair(fungx, index.family = "jaccard")
#fung.dist<-fd$beta.jtu
bd<-betadisper(fung.dist,factor(envx$TP))
print (anova(bd)); print(adonis(fung.dist~envx$TP))
plot(bd, col=blue2red(17)[3:17],
hull = FALSE,cex = 0.8,label.cex = 0.8,
seg.col=blue2red(17)[3:17], main="", xlab="PC1", ylab="PC2")
#boxplot(bd)
#print(TukeyHSD(bd))
}
## Analysis of Variance Table
##
## Response: Distances
## Df Sum Sq Mean Sq F value Pr(>F)
## Groups 14 0.68828 0.049163 20.932 < 2.2e-16 ***
## Residuals 75 0.17615 0.002349
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Call:
## adonis(formula = fung.dist ~ envx$TP)
##
## Permutation: free
## Number of permutations: 999
##
## Terms added sequentially (first to last)
##
## Df SumsOfSqs MeanSqs F.Model R2 Pr(>F)
## envx$TP 1 4.4815 4.4815 43.004 0.32827 0.001 ***
## Residuals 88 9.1706 0.1042 0.67173
## Total 89 13.6521 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Analysis of Variance Table
##
## Response: Distances
## Df Sum Sq Mean Sq F value Pr(>F)
## Groups 14 0.077915 0.0055654 3.5638 0.0001699 ***
## Residuals 75 0.117124 0.0015617
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Call:
## adonis(formula = fung.dist ~ envx$TP)
##
## Permutation: free
## Number of permutations: 999
##
## Terms added sequentially (first to last)
##
## Df SumsOfSqs MeanSqs F.Model R2 Pr(>F)
## envx$TP 1 2.8000 2.79999 32.156 0.26762 0.001 ***
## Residuals 88 7.6626 0.08707 0.73238
## Total 89 10.4626 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Analysis of Variance Table
##
## Response: Distances
## Df Sum Sq Mean Sq F value Pr(>F)
## Groups 14 0.037248 0.0026606 1.241 0.2651
## Residuals 75 0.160792 0.0021439
##
## Call:
## adonis(formula = fung.dist ~ envx$TP)
##
## Permutation: free
## Number of permutations: 999
##
## Terms added sequentially (first to last)
##
## Df SumsOfSqs MeanSqs F.Model R2 Pr(>F)
## envx$TP 1 2.9870 2.9870 28.049 0.2417 0.001 ***
## Residuals 88 9.3716 0.1065 0.7583
## Total 89 12.3587 1.0000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Analysis of Variance Table
##
## Response: Distances
## Df Sum Sq Mean Sq F value Pr(>F)
## Groups 14 0.044408 0.0031720 0.9021 0.56
## Residuals 75 0.263730 0.0035164
##
## Call:
## adonis(formula = fung.dist ~ envx$TP)
##
## Permutation: free
## Number of permutations: 999
##
## Terms added sequentially (first to last)
##
## Df SumsOfSqs MeanSqs F.Model R2 Pr(>F)
## envx$TP 1 0.5572 0.55723 5.4474 0.05829 0.001 ***
## Residuals 88 9.0017 0.10229 0.94171
## Total 89 9.5589 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
# jaccard
fung1<-fung[env$Treatment1=="Pre_flowering_drought"& env$TP>0,]
env1<-env[env$Treatment1=="Pre_flowering_drought"& env$TP>0,]
par(mfrow=c(1,4),mar=c(2, 2, 0.5, 0.5))
for (i in c("Leaf","Root", "Rhizosphere", "Soil")){
fungx<-fung1[env1$Treatment1== "Pre_flowering_drought" & env1$Habitat== i,]
envx<-env1[env1$Treatment1== "Pre_flowering_drought" & env1$Habitat== i,]
fung.dist<-vegdist(decostand(fungx,"hellinger"),"jaccard")
#fungx[fungx>0]=1
#fd<-beta.pair(fungx, index.family = "jaccard")
#fung.dist<-fd$beta.jtu
bd<-betadisper(fung.dist,factor(envx$TP))
print (anova(bd)); print(adonis(fung.dist~envx$TP))
plot(bd, col=blue2red(17)[3:17],
hull = FALSE,cex = 0.8,label.cex = 0.8,
seg.col=blue2red(17)[3:17], main="", xlab="PC1", ylab="PC2")
#boxplot(bd)
#print(TukeyHSD(bd))
}
## Analysis of Variance Table
##
## Response: Distances
## Df Sum Sq Mean Sq F value Pr(>F)
## Groups 14 0.76455 0.054611 27.434 < 2.2e-16 ***
## Residuals 75 0.14930 0.001991
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Call:
## adonis(formula = fung.dist ~ envx$TP)
##
## Permutation: free
## Number of permutations: 999
##
## Terms added sequentially (first to last)
##
## Df SumsOfSqs MeanSqs F.Model R2 Pr(>F)
## envx$TP 1 5.267 5.2670 28.86 0.24696 0.001 ***
## Residuals 88 16.060 0.1825 0.75304
## Total 89 21.327 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Analysis of Variance Table
##
## Response: Distances
## Df Sum Sq Mean Sq F value Pr(>F)
## Groups 14 0.086308 0.0061648 3.665 0.0001221 ***
## Residuals 75 0.126158 0.0016821
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Call:
## adonis(formula = fung.dist ~ envx$TP)
##
## Permutation: free
## Number of permutations: 999
##
## Terms added sequentially (first to last)
##
## Df SumsOfSqs MeanSqs F.Model R2 Pr(>F)
## envx$TP 1 3.426 3.4260 20.041 0.1855 0.001 ***
## Residuals 88 15.043 0.1709 0.8145
## Total 89 18.469 1.0000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Analysis of Variance Table
##
## Response: Distances
## Df Sum Sq Mean Sq F value Pr(>F)
## Groups 14 0.033625 0.0024018 1.2387 0.2666
## Residuals 75 0.145419 0.0019389
##
## Call:
## adonis(formula = fung.dist ~ envx$TP)
##
## Permutation: free
## Number of permutations: 999
##
## Terms added sequentially (first to last)
##
## Df SumsOfSqs MeanSqs F.Model R2 Pr(>F)
## envx$TP 1 3.383 3.3830 17.149 0.1631 0.001 ***
## Residuals 88 17.359 0.1973 0.8369
## Total 89 20.742 1.0000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Analysis of Variance Table
##
## Response: Distances
## Df Sum Sq Mean Sq F value Pr(>F)
## Groups 14 0.033524 0.0023946 0.9055 0.5564
## Residuals 75 0.198330 0.0026444
##
## Call:
## adonis(formula = fung.dist ~ envx$TP)
##
## Permutation: free
## Number of permutations: 999
##
## Terms added sequentially (first to last)
##
## Df SumsOfSqs MeanSqs F.Model R2 Pr(>F)
## envx$TP 1 0.8029 0.80292 4.2092 0.04565 0.001 ***
## Residuals 88 16.7863 0.19075 0.95435
## Total 89 17.5892 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
# turnover
fung1<-fung[env$Treatment1=="Pre_flowering_drought"& env$TP>0,]
env1<-env[env$Treatment1=="Pre_flowering_drought"& env$TP>0,]
par(mfrow=c(1,4),mar=c(2, 2, 0.5, 0.5))
for (i in c("Leaf","Root", "Rhizosphere", "Soil")){
fungx<-fung1[env1$Treatment1== "Pre_flowering_drought" & env1$Habitat== i,]
envx<-env1[env1$Treatment1== "Pre_flowering_drought" & env1$Habitat== i,]
#fung.dist<-vegdist(decostand(fungx,"hellinger"),"bray")
fungx[fungx>0]=1
fd<-beta.pair(fungx, index.family = "jaccard")
fung.dist<-fd$beta.jtu
bd<-betadisper(fung.dist,factor(envx$TP))
print (anova(bd)); print(adonis(fung.dist~envx$TP))
plot(bd, col=blue2red(17)[3:17],
hull = FALSE,cex = 0.8,label.cex = 0.8,
seg.col=blue2red(17)[3:17], main="", xlab="PC1", ylab="PC2")
#boxplot(bd)
#print(TukeyHSD(bd))
}
## Analysis of Variance Table
##
## Response: Distances
## Df Sum Sq Mean Sq F value Pr(>F)
## Groups 14 0.55669 0.039764 7.3274 2.516e-09 ***
## Residuals 75 0.40701 0.005427
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Call:
## adonis(formula = fung.dist ~ envx$TP)
##
## Permutation: free
## Number of permutations: 999
##
## Terms added sequentially (first to last)
##
## Df SumsOfSqs MeanSqs F.Model R2 Pr(>F)
## envx$TP 1 2.8433 2.84326 17.648 0.16704 0.001 ***
## Residuals 88 14.1779 0.16111 0.83296
## Total 89 17.0211 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Analysis of Variance Table
##
## Response: Distances
## Df Sum Sq Mean Sq F value Pr(>F)
## Groups 14 0.06210 0.0044355 0.828 0.637
## Residuals 75 0.40175 0.0053567
##
## Call:
## adonis(formula = fung.dist ~ envx$TP)
##
## Permutation: free
## Number of permutations: 999
##
## Terms added sequentially (first to last)
##
## Df SumsOfSqs MeanSqs F.Model R2 Pr(>F)
## envx$TP 1 2.943 2.94302 20.285 0.18733 0.001 ***
## Residuals 88 12.767 0.14508 0.81267
## Total 89 15.710 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Analysis of Variance Table
##
## Response: Distances
## Df Sum Sq Mean Sq F value Pr(>F)
## Groups 14 0.021592 0.0015423 0.566 0.8829
## Residuals 75 0.204353 0.0027247
##
## Call:
## adonis(formula = fung.dist ~ envx$TP)
##
## Permutation: free
## Number of permutations: 999
##
## Terms added sequentially (first to last)
##
## Df SumsOfSqs MeanSqs F.Model R2 Pr(>F)
## envx$TP 1 2.1945 2.19451 13.12 0.12975 0.001 ***
## Residuals 88 14.7187 0.16726 0.87025
## Total 89 16.9132 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Analysis of Variance Table
##
## Response: Distances
## Df Sum Sq Mean Sq F value Pr(>F)
## Groups 14 0.048035 0.0034311 1.1309 0.3464
## Residuals 75 0.227552 0.0030340
##
## Call:
## adonis(formula = fung.dist ~ envx$TP)
##
## Permutation: free
## Number of permutations: 999
##
## Terms added sequentially (first to last)
##
## Df SumsOfSqs MeanSqs F.Model R2 Pr(>F)
## envx$TP 1 0.5717 0.57166 3.5837 0.03913 0.001 ***
## Residuals 88 14.0375 0.15952 0.96087
## Total 89 14.6092 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
# nestedness
fung1<-fung[env$Treatment1=="Pre_flowering_drought"& env$TP>0,]
env1<-env[env$Treatment1=="Pre_flowering_drought"& env$TP>0,]
par(mfrow=c(1,4),mar=c(2, 2, 0.5, 0.5))
for (i in c("Leaf","Root", "Rhizosphere", "Soil")){
fungx<-fung1[env1$Treatment1== "Pre_flowering_drought" & env1$Habitat== i,]
envx<-env1[env1$Treatment1== "Pre_flowering_drought" & env1$Habitat== i,]
#fung.dist<-vegdist(decostand(fungx,"hellinger"),"bray")
fungx[fungx>0]=1
fd<-beta.pair(fungx, index.family = "jaccard")
fung.dist<-fd$beta.jne
bd<-betadisper(fung.dist,factor(envx$TP))
print (anova(bd)); print(adonis(fung.dist~envx$TP))
plot(bd, col=blue2red(17)[3:17],
hull = FALSE,cex = 0.8,label.cex = 0.8,
seg.col=blue2red(17)[3:17], main="", xlab="PC1", ylab="PC2")
#boxplot(bd)
#print(TukeyHSD(bd))
}
## Analysis of Variance Table
##
## Response: Distances
## Df Sum Sq Mean Sq F value Pr(>F)
## Groups 14 0.032897 0.0023498 2.4562 0.006591 **
## Residuals 75 0.071752 0.0009567
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Call:
## adonis(formula = fung.dist ~ envx$TP)
##
## Permutation: free
## Number of permutations: 999
##
## Terms added sequentially (first to last)
##
## Df SumsOfSqs MeanSqs F.Model R2 Pr(>F)
## envx$TP 1 0.18111 0.181112 31.645 0.26449 0.001 ***
## Residuals 88 0.50364 0.005723 0.73551
## Total 89 0.68475 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Analysis of Variance Table
##
## Response: Distances
## Df Sum Sq Mean Sq F value Pr(>F)
## Groups 14 0.019331 0.00138081 1.5455 0.1159
## Residuals 75 0.067008 0.00089344
##
## Call:
## adonis(formula = fung.dist ~ envx$TP)
##
## Permutation: free
## Number of permutations: 999
##
## Terms added sequentially (first to last)
##
## Df SumsOfSqs MeanSqs F.Model R2 Pr(>F)
## envx$TP 1 -0.048845 -0.048845 -14.013 -0.18939 1
## Residuals 88 0.306749 0.003486 1.18939
## Total 89 0.257904 1.00000
## Analysis of Variance Table
##
## Response: Distances
## Df Sum Sq Mean Sq F value Pr(>F)
## Groups 14 0.031381 0.0022415 2.5322 0.005129 **
## Residuals 75 0.066390 0.0008852
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Call:
## adonis(formula = fung.dist ~ envx$TP)
##
## Permutation: free
## Number of permutations: 999
##
## Terms added sequentially (first to last)
##
## Df SumsOfSqs MeanSqs F.Model R2 Pr(>F)
## envx$TP 1 -0.033409 -0.033409 -9.4031 -0.11964 1
## Residuals 88 0.312659 0.003553 1.11964
## Total 89 0.279251 1.00000
## Analysis of Variance Table
##
## Response: Distances
## Df Sum Sq Mean Sq F value Pr(>F)
## Groups 14 0.0078169 0.00055835 1.3984 0.1753
## Residuals 75 0.0299464 0.00039929
##
## Call:
## adonis(formula = fung.dist ~ envx$TP)
##
## Permutation: free
## Number of permutations: 999
##
## Terms added sequentially (first to last)
##
## Df SumsOfSqs MeanSqs F.Model R2 Pr(>F)
## envx$TP 1 -0.001394 -0.0013937 -1.2559 -0.01448 0.989
## Residuals 88 0.097652 0.0011097 1.01448
## Total 89 0.096259 1.00000
##
# bray
fung1<-fung[env$Treatment1=="Post_flowering_drought"& env$TP>0,]
env1<-env[env$Treatment1=="Post_flowering_drought"& env$TP>0,]
par(mfrow=c(1,4),mar=c(2, 2, 0.5, 0.5))
for (i in c("Leaf","Root", "Rhizosphere", "Soil")){
fungx<-fung1[env1$Treatment1== "Post_flowering_drought" & env1$Habitat== i,]
envx<-env1[env1$Treatment1== "Post_flowering_drought" & env1$Habitat== i,]
fung.dist<-vegdist(decostand(fungx,"hellinger"),"bray")
#fungx[fungx>0]=1
#fd<-beta.pair(fungx, index.family = "jaccard")
#fung.dist<-fd$beta.jtu
bd<-betadisper(fung.dist,factor(envx$TP))
print (anova(bd)); print(adonis(fung.dist~envx$TP))
plot(bd, col=blue2red(17)[8:17],
hull = FALSE,cex = 0.8,label.cex = 0.8,
seg.col=blue2red(17)[8:17], main="", xlab="PC1", ylab="PC2")
#boxplot(bd)
#print(TukeyHSD(bd))
}
## Analysis of Variance Table
##
## Response: Distances
## Df Sum Sq Mean Sq F value Pr(>F)
## Groups 9 0.1519 0.016878 5.5446 2.799e-05 ***
## Residuals 50 0.1522 0.003044
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Call:
## adonis(formula = fung.dist ~ envx$TP)
##
## Permutation: free
## Number of permutations: 999
##
## Terms added sequentially (first to last)
##
## Df SumsOfSqs MeanSqs F.Model R2 Pr(>F)
## envx$TP 1 0.6668 0.66684 12.902 0.18197 0.001 ***
## Residuals 58 2.9978 0.05169 0.81803
## Total 59 3.6646 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Analysis of Variance Table
##
## Response: Distances
## Df Sum Sq Mean Sq F value Pr(>F)
## Groups 9 0.006874 0.00076374 0.5299 0.8458
## Residuals 50 0.072060 0.00144119
##
## Call:
## adonis(formula = fung.dist ~ envx$TP)
##
## Permutation: free
## Number of permutations: 999
##
## Terms added sequentially (first to last)
##
## Df SumsOfSqs MeanSqs F.Model R2 Pr(>F)
## envx$TP 1 0.3718 0.37184 6.8904 0.10619 0.001 ***
## Residuals 58 3.1299 0.05396 0.89381
## Total 59 3.5018 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Analysis of Variance Table
##
## Response: Distances
## Df Sum Sq Mean Sq F value Pr(>F)
## Groups 9 0.005617 0.00062413 0.5569 0.8253
## Residuals 50 0.056040 0.00112079
##
## Call:
## adonis(formula = fung.dist ~ envx$TP)
##
## Permutation: free
## Number of permutations: 999
##
## Terms added sequentially (first to last)
##
## Df SumsOfSqs MeanSqs F.Model R2 Pr(>F)
## envx$TP 1 0.7288 0.72876 8.0412 0.12176 0.001 ***
## Residuals 58 5.2564 0.09063 0.87824
## Total 59 5.9852 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Analysis of Variance Table
##
## Response: Distances
## Df Sum Sq Mean Sq F value Pr(>F)
## Groups 9 0.008142 0.00090463 0.5291 0.8464
## Residuals 50 0.085487 0.00170974
##
## Call:
## adonis(formula = fung.dist ~ envx$TP)
##
## Permutation: free
## Number of permutations: 999
##
## Terms added sequentially (first to last)
##
## Df SumsOfSqs MeanSqs F.Model R2 Pr(>F)
## envx$TP 1 0.4408 0.44080 4.1771 0.06718 0.001 ***
## Residuals 58 6.1207 0.10553 0.93282
## Total 59 6.5615 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
# jaccard
fung1<-fung[env$Treatment1=="Post_flowering_drought"& env$TP>0,]
env1<-env[env$Treatment1=="Post_flowering_drought"& env$TP>0,]
par(mfrow=c(1,4),mar=c(2, 2, 0.5, 0.5))
for (i in c("Leaf","Root", "Rhizosphere", "Soil")){
fungx<-fung1[env1$Treatment1== "Post_flowering_drought" & env1$Habitat== i,]
envx<-env1[env1$Treatment1== "Post_flowering_drought" & env1$Habitat== i,]
fung.dist<-vegdist(decostand(fungx,"hellinger"),"jaccard")
#fungx[fungx>0]=1
#fd<-beta.pair(fungx, index.family = "jaccard")
#fung.dist<-fd$beta.jtu
bd<-betadisper(fung.dist,factor(envx$TP))
print (anova(bd)); print(adonis(fung.dist~envx$TP))
plot(bd, col=blue2red(17)[8:17],
hull = FALSE,cex = 0.8,label.cex = 0.8,
seg.col=blue2red(17)[8:17], main="", xlab="PC1", ylab="PC2")
#boxplot(bd)
#print(TukeyHSD(bd))
}
## Analysis of Variance Table
##
## Response: Distances
## Df Sum Sq Mean Sq F value Pr(>F)
## Groups 9 0.20873 0.0231921 5.7387 1.917e-05 ***
## Residuals 50 0.20207 0.0040414
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Call:
## adonis(formula = fung.dist ~ envx$TP)
##
## Permutation: free
## Number of permutations: 999
##
## Terms added sequentially (first to last)
##
## Df SumsOfSqs MeanSqs F.Model R2 Pr(>F)
## envx$TP 1 1.1262 1.1262 10.202 0.14958 0.001 ***
## Residuals 58 6.4031 0.1104 0.85042
## Total 59 7.5293 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Analysis of Variance Table
##
## Response: Distances
## Df Sum Sq Mean Sq F value Pr(>F)
## Groups 9 0.009335 0.0010372 0.5368 0.8406
## Residuals 50 0.096609 0.0019322
##
## Call:
## adonis(formula = fung.dist ~ envx$TP)
##
## Permutation: free
## Number of permutations: 999
##
## Terms added sequentially (first to last)
##
## Df SumsOfSqs MeanSqs F.Model R2 Pr(>F)
## envx$TP 1 0.6492 0.64916 5.3693 0.08473 0.001 ***
## Residuals 58 7.0123 0.12090 0.91527
## Total 59 7.6615 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Analysis of Variance Table
##
## Response: Distances
## Df Sum Sq Mean Sq F value Pr(>F)
## Groups 9 0.005528 0.00061428 0.5538 0.8277
## Residuals 50 0.055456 0.00110912
##
## Call:
## adonis(formula = fung.dist ~ envx$TP)
##
## Permutation: free
## Number of permutations: 999
##
## Terms added sequentially (first to last)
##
## Df SumsOfSqs MeanSqs F.Model R2 Pr(>F)
## envx$TP 1 0.9713 0.97126 5.4734 0.08623 0.001 ***
## Residuals 58 10.2922 0.17745 0.91377
## Total 59 11.2634 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Analysis of Variance Table
##
## Response: Distances
## Df Sum Sq Mean Sq F value Pr(>F)
## Groups 9 0.006899 0.00076655 0.5215 0.852
## Residuals 50 0.073499 0.00146998
##
## Call:
## adonis(formula = fung.dist ~ envx$TP)
##
## Permutation: free
## Number of permutations: 999
##
## Terms added sequentially (first to last)
##
## Df SumsOfSqs MeanSqs F.Model R2 Pr(>F)
## envx$TP 1 0.5849 0.58494 2.9734 0.04877 0.001 ***
## Residuals 58 11.4100 0.19672 0.95123
## Total 59 11.9950 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
# turnover
fung1<-fung[env$Treatment1=="Post_flowering_drought" & env$TP>0,]
env1<-env[env$Treatment1=="Post_flowering_drought" & env$TP>0,]
par(mfrow=c(1,4),mar=c(2, 2, 0.5, 0.5))
for (i in c("Leaf","Root", "Rhizosphere", "Soil")){
fungx<-fung1[env1$Treatment1== "Post_flowering_drought" & env1$Habitat== i,]
envx<-env1[env1$Treatment1== "Post_flowering_drought" & env1$Habitat== i,]
#fung.dist<-vegdist(decostand(fungx,"hellinger"),"bray")
fungx[fungx>0]=1
fd<-beta.pair(fungx, index.family = "jaccard")
fung.dist<-fd$beta.jtu
bd<-betadisper(fung.dist,factor(envx$TP))
print (anova(bd)); print(adonis(fung.dist~envx$TP))
plot(bd, col=blue2red(17)[8:17],
hull = FALSE,cex = 0.8,label.cex = 0.8,
seg.col=blue2red(17)[8:17], main="", xlab="PC1", ylab="PC2")
#boxplot(bd)
#print(TukeyHSD(bd))
}
## Analysis of Variance Table
##
## Response: Distances
## Df Sum Sq Mean Sq F value Pr(>F)
## Groups 9 0.30295 0.033661 9.7552 1.991e-08 ***
## Residuals 50 0.17253 0.003451
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Call:
## adonis(formula = fung.dist ~ envx$TP)
##
## Permutation: free
## Number of permutations: 999
##
## Terms added sequentially (first to last)
##
## Df SumsOfSqs MeanSqs F.Model R2 Pr(>F)
## envx$TP 1 -0.4917 -0.49174 -4.6342 -0.08684 1
## Residuals 58 6.1545 0.10611 1.08684
## Total 59 5.6627 1.00000
## Analysis of Variance Table
##
## Response: Distances
## Df Sum Sq Mean Sq F value Pr(>F)
## Groups 9 0.03166 0.0035178 1.0019 0.451
## Residuals 50 0.17556 0.0035113
##
## Call:
## adonis(formula = fung.dist ~ envx$TP)
##
## Permutation: free
## Number of permutations: 999
##
## Terms added sequentially (first to last)
##
## Df SumsOfSqs MeanSqs F.Model R2 Pr(>F)
## envx$TP 1 0.5069 0.50688 3.8301 0.06195 0.001 ***
## Residuals 58 7.6757 0.13234 0.93805
## Total 59 8.1826 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Analysis of Variance Table
##
## Response: Distances
## Df Sum Sq Mean Sq F value Pr(>F)
## Groups 9 0.009326 0.0010363 0.4272 0.9142
## Residuals 50 0.121293 0.0024259
##
## Call:
## adonis(formula = fung.dist ~ envx$TP)
##
## Permutation: free
## Number of permutations: 999
##
## Terms added sequentially (first to last)
##
## Df SumsOfSqs MeanSqs F.Model R2 Pr(>F)
## envx$TP 1 0.6514 0.65136 3.7535 0.06078 0.001 ***
## Residuals 58 10.0648 0.17353 0.93922
## Total 59 10.7162 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Analysis of Variance Table
##
## Response: Distances
## Df Sum Sq Mean Sq F value Pr(>F)
## Groups 9 0.006556 0.00072844 0.5421 0.8367
## Residuals 50 0.067192 0.00134384
##
## Call:
## adonis(formula = fung.dist ~ envx$TP)
##
## Permutation: free
## Number of permutations: 999
##
## Terms added sequentially (first to last)
##
## Df SumsOfSqs MeanSqs F.Model R2 Pr(>F)
## envx$TP 1 0.3550 0.35503 2.0926 0.03482 0.001 ***
## Residuals 58 9.8404 0.16966 0.96518
## Total 59 10.1955 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
# nestedness
fung1<-fung[env$Treatment1=="Post_flowering_drought" & env$TP>0,]
env1<-env[env$Treatment1=="Post_flowering_drought" & env$TP>0,]
par(mfrow=c(1,4),mar=c(2, 2, 0.5, 0.5))
for (i in c("Leaf","Root", "Rhizosphere", "Soil")){
fungx<-fung1[env1$Treatment1== "Post_flowering_drought" & env1$Habitat== i,]
envx<-env1[env1$Treatment1== "Post_flowering_drought" & env1$Habitat== i,]
#fung.dist<-vegdist(decostand(fungx,"hellinger"),"bray")
fungx[fungx>0]=1
fd<-beta.pair(fungx, index.family = "jaccard")
fung.dist<-fd$beta.jne
bd<-betadisper(fung.dist,factor(envx$TP))
print (anova(bd)); print(adonis(fung.dist~envx$TP))
plot(bd, col=blue2red(17)[8:17],
hull = FALSE,cex = 0.8,label.cex = 0.8,
seg.col=blue2red(17)[8:17], main="", xlab="PC1", ylab="PC2")
#boxplot(bd)
#print(TukeyHSD(bd))
}
## Analysis of Variance Table
##
## Response: Distances
## Df Sum Sq Mean Sq F value Pr(>F)
## Groups 9 0.077257 0.0085841 2.43 0.02236 *
## Residuals 50 0.176628 0.0035326
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Call:
## adonis(formula = fung.dist ~ envx$TP)
##
## Permutation: free
## Number of permutations: 999
##
## Terms added sequentially (first to last)
##
## Df SumsOfSqs MeanSqs F.Model R2 Pr(>F)
## envx$TP 1 0.63415 0.63415 63.152 0.52126 0.001 ***
## Residuals 58 0.58241 0.01004 0.47874
## Total 59 1.21656 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Analysis of Variance Table
##
## Response: Distances
## Df Sum Sq Mean Sq F value Pr(>F)
## Groups 9 0.009575 0.0010639 0.9354 0.5034
## Residuals 50 0.056871 0.0011374
##
## Call:
## adonis(formula = fung.dist ~ envx$TP)
##
## Permutation: free
## Number of permutations: 999
##
## Terms added sequentially (first to last)
##
## Df SumsOfSqs MeanSqs F.Model R2 Pr(>F)
## envx$TP 1 0.004873 0.0048730 1.3251 0.02234 0.293
## Residuals 58 0.213298 0.0036776 0.97766
## Total 59 0.218171 1.00000
## Analysis of Variance Table
##
## Response: Distances
## Df Sum Sq Mean Sq F value Pr(>F)
## Groups 9 0.005857 0.00065073 0.9589 0.4845
## Residuals 50 0.033929 0.00067858
##
## Call:
## adonis(formula = fung.dist ~ envx$TP)
##
## Permutation: free
## Number of permutations: 999
##
## Terms added sequentially (first to last)
##
## Df SumsOfSqs MeanSqs F.Model R2 Pr(>F)
## envx$TP 1 -0.001655 -0.0016552 -0.70514 -0.01231 0.92
## Residuals 58 0.136142 0.0023473 1.01231
## Total 59 0.134486 1.00000
## Analysis of Variance Table
##
## Response: Distances
## Df Sum Sq Mean Sq F value Pr(>F)
## Groups 9 0.020215 0.00224615 2.4336 0.02218 *
## Residuals 50 0.046148 0.00092297
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Call:
## adonis(formula = fung.dist ~ envx$TP)
##
## Permutation: free
## Number of permutations: 999
##
## Terms added sequentially (first to last)
##
## Df SumsOfSqs MeanSqs F.Model R2 Pr(>F)
## envx$TP 1 -0.006923 -0.0069226 -3.275 -0.05985 1
## Residuals 58 0.122598 0.0021138 1.05985
## Total 59 0.115675 1.00000
